What are Narratives?
A narrative focuses on a sense-making exploration of cause-effect relationships to confer meaning on reality. Narratives are the “solution to the problem of translating knowing into telling.” In the narratives of science, scientists develop a claim or argument, supported by pieces of evidence, a series of data.
Data science shapes narratives to give representations of the world, to express and explain past, current, or future phenomena, and calls for action. Data science not only lends support to or challenges narratives through its analyses, it also defines what types of narratives are possible and credible and which are amplified, silenced, or rendered incomprehensible.
Narratives are shaped by individuals but are collective and embedded in the social fabric: they are shared, enacted, reflected, and materialized in a socio-technical system.
Narratives in action
Narratives such as “gender is binary” play a key role in people's lives, including how they communicate, decide what and who should be valued, educate children, and build the physical environment. It shapes school curricula and informal ways in which children are taught to behave, and it is materialized in the layout of bathrooms and public spaces.
The “gender is binary” narrative informs what people assume about the other person upon seeing them and how they refer to them. In addition to influencing social life, narratives also have a bearing on what kind of scientific research is deemed beneficial to society, the research questions that researchers pose, and what they look for and find in their data.
Because narratives reinforce one another in a society, it may be difficult to transform one narrative without impacting others simultaneously. Identifying the shared foundations of narratives can be a way to discover the necessary sites of intervention to make a change.
Ways to reflect on narratives
- What narratives motivated and informed the creation of this data?
- What underlying beliefs indicate that data science approaches are appropriate for the question or problem?
- What kind of change is the work implicitly or explicitly intended to bring about?
- What narrative is the result of your data science work contributing to?
- Who do you anticipate will agree with this narrative?
- Who will have questions about it?
- Who will resist it?
- Why is the data you have produced the most interesting to answer your research question?
- What other alternatives might exist?
The concept of narratives refers to prominently circulated or widely held beliefs about why the world is as it is, what changes are possible and worthwhile, and what should be done to achieve the desired future. Narratives are profoundly crucial in human life for individual and collective sense-making and accounting (Ricœur, 1988; Butler, 2005; Meretoja, 2018) and are constructed around any and every facet of existence. In the context of the data science workflow, we are mainly concerned with how narratives garner support for technological and scientific interventions and how, in turn, technologies underwrite visions of futures worth wanting and working toward (Jasanoff & Kim, 2015). For example, researchers have found that data are commonly portrayed as an abundant resource that may be exploited to improve nearly any aspect of life by providing convincing analysis that sheds light on complex social or natural phenomena, thereby preparing the path for evidence-based decisions (Puschmann & Burgess, 2014; Stark & Hoffmann, 2019). The tight synergy in this narrative between the vision of social well-being, sound decision-making, and data gives data science significant explanatory power and world-building capacity. This same coupling also tends to erase or relegate to obscurity and falsehood perspectives, explanations, and visions that do not conform to accepted narratives.